Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches

King, Robert; Rughooputh, H.C.S.; Deb, Kalyanmoy

doi:10.1007/978-3-540-31880-4_47

Cited by 81 publications

(46 citation statements)

References 17 publications

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“…The total power generated must supply the total load demand and the transmission losses [19]. The real power loss P loss can be calculated from NewtonRaphson load flow solution, which gives all bus voltage magnitudes and angles; it can be described as follows [22]:…”

Section: Power Balance Constraintmentioning

confidence: 99%

“…NSGA suffers from computational complexity, non-elitist approach and the need to specify a sharing parameter. An improved version of NSGA known as NSGA-II, which resolves CEED problems and uses elitism to create a diverse Pareto-optimal front, has been subsequently presented [18][19][20][21]. In addition, TOPSIS method is employed to choose the BCS, which will be useful to the decision maker [20,21].…”

Section: Introductionmentioning

confidence: 99%

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NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

Rajkumar¹,

Mahadevan²,

Kannan³

et al. 2014

Journal of Electrical Engineering and Technology

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-Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

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Section: Power Balance Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

Rajkumar¹,

Mahadevan²,

Kannan³

et al. 2014

Journal of Electrical Engineering and Technology

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“…The main drawback is that it only focuses on the present demand and cost values, and ignores the longer term situation. From a different perspective of the problem, [12,20,24] focus on the economic dispatch of electricity such that the total fuel cost is minimized, together with the total emissions of CO 2 and N O x . As such they do not consider the park placement problem but rather the source of RE to consider to reach the objectives.…”

Section: Related Workmentioning

confidence: 99%

“…Note that there is one constraint per dimension. A solution to the MKP is one that satisfies the conjunction of the m constraints (12). An optimal solution is one that maximizes (11).…”

Section: Multi-dimensional Knapsack Equivalencementioning

confidence: 99%

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Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model

Gervet

Atef

2013

RAIRO-Oper. Res.

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RésuméLa recherche appliquée enénergies renouvelables soulève des défis complexes de nature technologique, economique ou politique. Dans cet article, nous adressons le problème technico-économique de sélection optimale d'emplacements de parcséoliens et solaires en Egypte, de telle sorte que la demande enélectricité soit satisfaiteà coût minimum. Ultimement, notre objectif est de construire un outil d'aideà la décision qui va pourvoir aux investisseurs privés et gouvernementaux, une aide précieuse pour prendre des décisionsà court et long terme relativementà la création de tels parcs. Les approches existantes raisonnent essentiellementà partir de données passées. Dans cet article, nous introduisons une nouvelle approche qui considèreà la fois les données passées et futures, et montrons l'impact de la prise en compte des deux types de données dans une modèle d'optimisation en deux temps. Nous montrons que la Programmation Linéaire par Entiers est une mé-thode efficace pour résoudre le problème avec des données passées ainsi que le modèle en deux temps qui prend en compte les données prédites, rajoutant de nouvelles contraintes au modèle initial. Notreévaluation montre que le modèle en deux temps améliore la solution avec une vision plus globale et un meilleur coût total.

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Environmental and economic power dispatch of thermal generators using modified NSGA-II algorithm

Muthuswamy¹,

Mahadevan

Subramanian

et al. 2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents the solution to the problem in fabricating Environmental and Economic Power Dispatch (EEPD) of thermal generators with valve‐point loading effect and multiple prohibited operating zones (POZ). The valve‐point effect introduces ripples in the input–output characteristics of generating units, and the existence of POZ breaks the operating region of a generating unit into isolated sub‐regions, thus forms a nonconvex decision space. The EEPD problem becomes a nonsmooth optimization problem because of these valve‐point effect and POZ. Accuracy of the solution for a practical system is improved by considering the nonlinearities of valve‐point loading effect and multiple POZ in the EEPD problem. The multi‐objective evolutionary algorithms, namely non‐dominated sorting genetic algorithm‐II (NSGA‐II) and modified NSGA‐II (MNSGA‐II) have been applied for solving the multi‐objective nonlinear optimization EEPD problem. To improve the uniform distribution of non‐dominated solutions, dynamic crowding distance is considered in the NSGA‐II and developed MNSGA‐II. These multi‐objective evolutionary algorithms have been individually examined and applied to the standard IEEE 30‐bus and IEEE 118‐bus test systems. Real‐coded genetic algorithm is used to generate reference Pareto‐front, which is used to compare with the Pareto front obtained using NSGA‐II and MNSGA‐II. Numerical results reveal that MNSGA‐II is effectively capable for appreciable performance than NSGA‐II to solve the different power system nonsmooth EEPD problem. Moreover, three different performance metrics such as convergence, diversity and Inverted Generational Distance are calculated for the evaluation of closeness of obtained Pareto fronts to the reference Pareto‐front. In addition, an approach based on Technique for ordering Preferences by Similarity to Ideal Solution is applied to extract best compromise solution from the obtained non‐domination solutions. Copyright © 2014 John Wiley & Sons, Ltd.

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Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches

Cited by 81 publications

References 17 publications

NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model

Environmental and economic power dispatch of thermal generators using modified NSGA-II algorithm

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